Pixel-based similarity metrics like euclidean distance and patch-wise cross-correlation are well explored within algorithmic [1]}, [2]}, [3]}, [4]}, [5]}, [6]} and deep learning based [7]}, [8]}, [9]}, [10]}, [11]}, [12]}, [13]}, [14]}, [15]}, [16]}, [17]} image registration.
These metrics assume that if the image intensities are aligned, or strongly correlated, the images are well aligned. Each choice of metric adds additional assumptions on the characteristics of the specific dataset. Thus, a common methodological approach is to trial registration models with multiple different pixel-based metrics, and choose the metric performing best on the dataset [8]}, [14]}.
